Several disease affecting the human respiratory system, such as asthma, pneumonia, etc. are associated with distinctive sounds. Here, we propose a methodology towards their automatic identification by means of signal processing and pattern recognition algorithms. We designed a suitable feature set based on wavelet packet analysis characterizing data coming from diverse classes of respiratory sounds following the logic of the challenge organised within the International Conference on Biomedical Health Informatics in 2017. The patterns revealed by the feature extraction stage are modelled by hidden Markov models. Automatic identification is carried out via a directed acyclic graph (DAG) scheme limiting the problem space while based on decisions made by the available HMMs. Thorough experiments following a well-established protocol demonstrate the efficacy of the proposed solution. Indeed, such a DAG-based structure outperforms the current state of the art including deep networks based on convolutional kernels. Importantly, the presented solution offers a high level of explainability as one is able to investigate the effective DAG path and understand both correct and incorrect predictions.
Automatic acoustic identification of respiratory diseases / S. Ntalampiras, I. Potamitis. - In: EVOLVING SYSTEMS. - ISSN 1868-6478. - 12:1 Special issue(2021 Mar), pp. 69-77.
|Titolo:||Automatic acoustic identification of respiratory diseases|
NTALAMPIRAS, STAVROS (Primo) (Corresponding)
|Parole Chiave:||Respiratory sound classification; Acoustic signal processing; Respiratory diseases; Directed acyclic graph; Hidden Markov model;|
|Settore Scientifico Disciplinare:||Settore INF/01 - Informatica|
|Data di pubblicazione:||mar-2021|
|Data ahead of print / Data di stampa:||apr-2020|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1007/s12530-020-09339-0|
|Appare nelle tipologie:||01 - Articolo su periodico|
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